About

Benchmarks

TREND DATASET BEST METHOD PAPER TITLE PAPER CODE COMPARE

Datasets

Greatest papers with code

Med3D: Transfer Learning for 3D Medical Image Analysis

1 Apr 2019Tencent/MedicalNet

The performance on deep learning is significantly affected by volume of training data.

3D MEDICAL IMAGING SEGMENTATION LIVER SEGMENTATION TRANSFER LEARNING

Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysis

19 Aug 2019MrGiovanni/ModelsGenesis

More importantly, learning a model from scratch simply in 3D may not necessarily yield performance better than transfer learning from ImageNet in 2D, but our Models Genesis consistently top any 2D approaches including fine-tuning the models pre-trained from ImageNet as well as fine-tuning the 2D versions of our Models Genesis, confirming the importance of 3D anatomical information and significance of our Models Genesis for 3D medical imaging.

BRAIN TUMOR SEGMENTATION LIVER SEGMENTATION LUNG NODULE DETECTION LUNG NODULE SEGMENTATION PULMONARY EMBOLISM DETECTION SELF-SUPERVISED LEARNING TRANSFER LEARNING

H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation from CT Volumes

21 Sep 2017xmengli999/H-DenseUNet

Our method outperformed other state-of-the-arts on the segmentation results of tumors and achieved very competitive performance for liver segmentation even with a single model.

AUTOMATIC LIVER AND TUMOR SEGMENTATION LESION SEGMENTATION LIVER SEGMENTATION TUMOR SEGMENTATION

KiU-Net: Overcomplete Convolutional Architectures for Biomedical Image and Volumetric Segmentation

4 Oct 2020jeya-maria-jose/KiU-Net-pytorch

To overcome this issue, we propose using an overcomplete convolutional architecture where we project our input image into a higher dimension such that we constrain the receptive field from increasing in the deep layers of the network.

3D MEDICAL IMAGING SEGMENTATION BRAIN TUMOR SEGMENTATION COMPUTED TOMOGRAPHY (CT) LIVER SEGMENTATION SEMANTIC SEGMENTATION ULTRASOUND VOLUMETRIC MEDICAL IMAGE SEGMENTATION

Learning Semantics-enriched Representation via Self-discovery, Self-classification, and Self-restoration

14 Jul 2020JLiangLab/SemanticGenesis

To this end, we train deep models to learn semantically enriched visual representation by self-discovery, self-classification, and self-restoration of the anatomy underneath medical images, resulting in a semantics-enriched, general-purpose, pre-trained 3D model, named Semantic Genesis.

BRAIN TUMOR SEGMENTATION CLASSIFICATION LIVER SEGMENTATION LUNG NODULE DETECTION LUNG NODULE SEGMENTATION REPRESENTATION LEARNING SELF-SUPERVISED LEARNING TRANSFER LEARNING

The Liver Tumor Segmentation Benchmark (LiTS)

13 Jan 2019andreped/livermask

The best liver segmentation algorithm achieved a Dice score of 0. 96(MICCAI) whereas for tumor segmentation the best algorithm evaluated at 0. 67(ISBI) and 0. 70(MICCAI).

COMPUTED TOMOGRAPHY (CT) LIVER SEGMENTATION TUMOR SEGMENTATION

Anatomy-guided Multimodal Registration by Learning Segmentation without Ground Truth: Application to Intraprocedural CBCT/MR Liver Segmentation and Registration

14 Apr 2021bbbbbbzhou/APA2Seg-Net

Our experimental results on in-house TACE patient data demonstrated that our APA2Seg-Net can generate robust CBCT and MR liver segmentation, and the anatomy-guided registration framework with these segmenters can provide high-quality multimodal registrations.

DOMAIN ADAPTATION IMAGE REGISTRATION LIVER SEGMENTATION

Fully Automatic Liver Attenuation Estimation Combing CNN Segmentation and Morphological Operations

23 Jun 2019MASILab/ALARM

Manually tracing regions of interest (ROIs) within the liver is the de facto standard method for measuring liver attenuation on computed tomography (CT) in diagnosing nonalcoholic fatty liver disease (NAFLD).

COMPUTED TOMOGRAPHY (CT) LIVER SEGMENTATION

Optimal input configuration of dynamic contrast enhanced MRI in convolutional neural networks for liver segmentation

22 Aug 2019MarielleJansen/Liver-segmentation

In this study, the optimal input configuration of DCE MR images for convolutional neural networks (CNNs) is studied.

LIVER SEGMENTATION